A detailed discussion of background information is set forth in parent applications, U.S. patent application Ser. Nos. 09/679,317, 10/822,445 and 11/034,325, all of which are incorporated by reference herein. Some more pertinent background is set forth below. All of the patents, patent applications, technical papers and other references mentioned below and in the parent applications are incorporated herein by reference in their entirety. No admission is made that any or all of these references are prior art and indeed, it is contemplated that they may not be available as prior art when interpreting 35 U.S.C. §102 in consideration of the claims of the present application.
“Pattern recognition” as used herein will generally mean any system which processes a signal that is generated by an object (e.g., representative of a pattern of returned or received impulses, waves or other physical property specific to and/or characteristic of and/or representative of that object) or is modified by interacting with an object, in order to determine to which one of a set of classes that the object belongs. Such a system might determine only that the object is or is not a member of one specified class, or it might attempt to assign the object to one of a larger set of specified classes, or find that it is not a member of any of the classes in the set. The signals processed are generally a series of electrical signals coming from transducers that are sensitive to acoustic (ultrasonic) or electromagnetic radiation (e.g., visible light, infrared radiation, capacitance or electric and/or magnetic fields), although other sources of information are frequently included. Pattern recognition systems generally involve the creation of a set of rules that permit the pattern to be recognized. These rules can be created by fuzzy logic systems, statistical correlations, or through sensor fusion methodologies as well as by trained pattern recognition systems such as neural networks, combination neural networks, cellular neural networks or support vector machines.
“Neural network” as used herein, unless stated otherwise, will generally mean a single neural network, a combination neural network, a cellular neural network, a support vector machine or any combinations thereof. For the purposes herein, a “neural network” is defined to include all such learning systems including cellular neural networks, support vector machines and other kernel-based learning systems and methods, cellular automata and all other pattern recognition methods and systems that learn. A “combination neural network” as used herein will generally apply to any combination of two or more neural networks as most broadly defined that are either connected together or that analyze all or a portion of the input data.
A “combination neural network” as used herein will generally apply to any combination of two or more neural networks that are either connected together or that analyze all or a portion of the input data. A combination neural network can be used to divide up tasks in solving a particular object sensing and identification problem. For example, one neural network can be used to identify an object occupying a space at the side of an automobile and a second neural network can be used to determine the position of the object or its location with respect to the vehicle, for example, in the blind spot. In another case, one neural network can be used merely to determine whether the data is similar to data upon which a main neural network has been trained or whether there is something significantly different about this data and therefore that the data should not be analyzed. Combination neural networks can sometimes be implemented as cellular neural networks. What has been described above is generally referred to as modular neural networks with and without feedback. Actually, the feedback does not have to be from the output to the input of the same neural network. The feedback from a downstream neural network could be input to an upstream neural network, for example. The neural networks can be combined in other ways, for example in a voting situation. Sometimes the data upon which the system is trained is sufficiently complex or imprecise that different views of the data will give different results. For example, a subset of transducers may be used to train one neural network and another subset to train a second neural network etc. The decision can then be based on a voting of the parallel neural networks, sometimes known as an ensemble neural network. In the past, neural networks have usually only been used in the form of a single neural network algorithm for identifying the occupancy state of the space near an automobile.
A trainable or a trained pattern recognition system as used herein generally means a pattern recognition system that is taught to recognize various patterns constituted within the signals by subjecting the system to a variety of examples. The most successful such system is the neural network used either singly or as a combination of neural networks. Thus, to generate the pattern recognition algorithm, test data is first obtained which constitutes a plurality of sets of returned waves, or wave patterns, or other information radiated or obtained from an object (or from the space in which the object will be situated in the passenger compartment, i.e., the space above the seat) and an indication of the identify of that object. A number of different objects are tested to obtain the unique patterns from each object. As such, the algorithm is generated, and stored in a computer processor, and which can later be applied to provide the identity of an object based on the wave pattern being received during use by a receiver connected to the processor and other information. For the purposes here, the identity of an object sometimes applies to not only the object itself but also to its location and/or orientation and velocity in the vicinity of the vehicle. For example, a vehicle that is stopped but pointing at the side of the host vehicle is different from the same vehicle that is approaching at such a velocity as to impact the host vehicle. Not all pattern recognition systems are trained systems and not all trained systems are neural networks. Other pattern recognition systems are based on fuzzy logic, sensor fusion, Kalman filters, correlation as well as linear and non-linear regression. Still other pattern recognition systems are hybrids of more than one system such as neural-fuzzy systems.
A pattern recognition algorithm will thus generally mean an algorithm applying or obtained using any type of pattern recognition system, e.g., a neural network, sensor fusion, fuzzy logic, etc.
To “identify” as used herein will generally mean to determine that the object belongs to a particular set or class. The class may be one containing, for example, all motorcycles, one containing all trees, or all trees in the path of the host vehicle depending on the purpose of the system.
To “ascertain the identity of” as used herein with reference to an object will generally mean to determine the type or nature of the object (obtain information as to what the object is), i.e., that the object is an car, a car on a collision course with the host vehicle, a truck, a tree, a pedestrian, a deer etc.
A “rear seat” of a vehicle as used herein will generally mean any seat behind the front seat on which a driver sits. Thus, in minivans or other large vehicles where there are more than two rows of seats, each row of seats behind the driver is considered a rear seat and thus there may be more than one “rear seat” in such vehicles. The space behind the front seat includes any number of such rear seats as well as any trunk spaces or other rear areas such as are present in station wagons.
In the description herein on anticipatory sensing, the term “approaching” when used in connection with the mention of an object or vehicle approaching another will usually mean the relative motion of the object toward the vehicle having the anticipatory sensor system. Thus, in a side impact with a tree, the tree will be considered as approaching the side of the vehicle and impacting the vehicle. In other words, the coordinate system used in general will be a coordinate system residing in the target vehicle. The “target” vehicle is the vehicle that is being impacted. This convention permits a general description to cover all of the cases such as where (i) a moving vehicle impacts into the side of a stationary vehicle, (ii) where both vehicles are moving when they impact, or (iii) where a vehicle is moving sideways into a stationary vehicle, tree or wall.
“Vehicle” as used herein includes any container that is movable either under its own power or using power from another vehicle. It includes, but is not limited to, automobiles, trucks, railroad cars, ships, airplanes, trailers, shipping containers, barges, etc. The word “container” will frequently be used interchangeably with vehicle however a container will generally mean that part of a vehicle that separate from and in some cases may exist separately and away from the source of motive power. Thus, a shipping container may exist in a shipping yard and a trailer may be parked in a parking lot without the tractor. The passenger compartment or a trunk of an automobile, on the other hand, are compartments of a container that generally only exists attaches to the vehicle chassis that also has an associated engine for moving the vehicle. Note a container can have one or a plurality of compartments.
“Transducer” or “transceiver” as used herein will generally mean the combination of a transmitter and a receiver. In come cases, the same device will serve both as the transmitter and receiver while in others two separate devices adjacent to each other will be used. In some cases, a transmitter is not used and in such cases transducer will mean only a receiver. Transducers include, for example, capacitive, inductive, ultrasonic, electromagnetic (antenna, CCD, CMOS arrays, laser, radar transmitter, terahertz transmitter and receiver, focal plane array, pin or avalanche diode, etc.), electric field, weight measuring or sensing devices. In some cases, a transducer will be a single pixel either acting alone, in a linear or an array of some other appropriate shape. In some cases, a transducer may comprise two parts such as the plates of a capacitor or the antennas of an electric field sensor. Sometimes, one antenna or plate will communicate with several other antennas or plates and thus for the purposes herein, a transducer will be broadly defined to refer, in most cases, to any one of the plates of a capacitor or antennas of a field sensor and in some other cases a pair of such plates or antennas will comprise a transducer as determined by the context in which the term is used.
A “wave sensor” or “wave transducer” is generally any device which senses either ultrasonic or electromagnetic waves. An electromagnetic wave sensor, for example, includes devices that sense any portion of the electromagnetic spectrum from ultraviolet down to a few hertz. The most commonly used kinds of electromagnetic wave sensors include CCD and CMOS arrays for sensing visible and/or infrared waves, millimeter wave and microwave radar, and capacitive or electric and/or magnetic field monitoring sensors that rely on the dielectric constant of the object occupying a space but also rely on the time variation of the field, expressed by waves as defined below, to determine a change in state.
A “CCD” will be defined to include all devices, including CMOS arrays, APS arrays, QWIP arrays or equivalent, artificial retinas and particularly HDRC arrays, which are capable of converting light frequencies, including infrared, visible and ultraviolet, into electrical signals. The particular CCD array used for many of the applications disclosed herein is implemented on a single chip that is less than two centimeters on a side. Data from the CCD array is digitized and sent serially to an electronic circuit (at times designated 120 herein) containing a microprocessor for analysis of the digitized data. In order to minimize the amount of data that needs to be stored, initial processing of the image data takes place as it is being received from the CCD array, as discussed in more detail above. In some cases, some image processing can take place on the chip such as described in a Kage et al. artificial retina article referenced in parent applications.
An “occupant protection apparatus” is any device, apparatus, system or component which is actuatable or deployable or includes a component which is actuatable or deployable for the purpose of attempting to reduce injury to the occupant in the event of a crash, rollover or other potential injurious event involving a vehicle
Inertial measurement unit (IMU), inertial navigation system (INS) and inertial reference unit (IRU) will in general be used be used interchangeably to mean a device having a plurality of accelerometers and a plurality of gyroscopes generally within the same package. Usually such a device will contain 3 accelerometers and 3 gyroscopes. In some cases a distinction will be made whereby the INS relates to an IMU or an IRU plus additional sensors and software such as a GPS, speedometer, odometer or other sensors plus optimizing software which may be based on a Kalman filter.
A precise positioning system or PPS is a system based on some information, usually of a physical nature, in the infrastructure that determines the precise location of a vehicle independently of a GPS-based system or the IMU. Such a system is employed as a vehicle is traveling and passes a particular location. A PPS can make use of various technologies including radar, laser radar, terahertz radar, RFID tags located in the infrastructure, MIR transmitters and receivers. Such locations are identified on a map database resident within the vehicle. In one case, for example, the map database contains data from a terahertz radar continuous scan of the environment to the side of a vehicle from a device located on a vehicle and pointed 45 degrees up relative to the horizontal plane. The map database contains the exact location of the vehicle that corresponds to the scan. Another vehicle can then determine its location by comparing its scan data with that stored with the map database and when there is a match, the vehicle knows its location. Of course many other technologies can be used to accomplish a similar result.
Unless stated otherwise, laser radar, lidar and ladar will be considered equivalent herein. In all cases, they represent a projected laser beam, which can be in the visual part of the electromagnetic spectrum but generally will be the infrared part of the electromagnetic spectrum and usually in the near infrared wavelengths. The projected laser beam can emanate from the optics as a nearly parallel beam or as a beam that diverges at any desired angle from less than zero degrees to ten or more of degrees depending on the application. A particular implementation may use a laser beam that at one time diverges at an angle less than one degree and at another time may diverge at several degrees using adjustable optics. The laser beam can have a diameter as it leaves the vehicle ranging from less than a millimeter to several centimeters. The above represent typical or representative ranges of dimensions but this invention is not limited by these ranges.